In this project, I analyzed Cyclitic bike rider data using Python to answer three key questions. Firstly, I investigated the differences in usage
patterns between annual members and casual riders. Secondly, I explored why casual riders may choose to purchase annual memberships. Finally, I
examined how Cyclitic can use digital media to encourage casual riders to become members. The project demonstrates my proficiency in using Python
for data analysis and my ability to draw meaningful insights from complex datasets. The project report is available on a public repository for others
to review and replicate the analysis process and gives positve feedback.
In this project, I conducted an exploratory data analysis on a Netflix dataset from May 2022 using Python. After cleaning the data, I analyzed several
aspects of the dataset, including age recommendation, word cloud analysis, correlation between TMDB score and popularity, and top countries and genres.
I found that the majority of movies/shows on Netflix are recommended for ages 13+, with love, life, and last being common themes in titles and
descriptions. Additionally, the top countries for content on Netflix are the USA and India, with comedy being the most common genre followed by
drama and documentary. This project demonstrates my ability to extract insights from complex datasets and use data visualization tools effectively.
The project report and code are available on a public repository for others to review and replicate the analysis process.
I completed a web scraping project to gather PSL (Pakistan Super League) cricket data from Cricinfo's website. Specifically, I collected information on bowling,
batting, players, teams, and rankings for each season from 2016 to 2022. The data was scraped using Python and can be found in a public repository along with the
code used for scraping.
I conducted an exploratory data analysis (EDA) of the European Soccer Database using Python. The database contains detailed information on soccer matches,
teams, players, and more from various European countries. In my analysis, I utilized various Python libraries such as Pandas, Matplotlib, sqlite3,
and Seaborn to explore the data and gain insights. The findings from the analysis can be found in a public repository, along with the code
used for the analysis.
This project involved analyzing a dataset of data science salaries, which was obtained from Kaggle. Using Python, I explored various factors that could
affect salaries, such as job title, years of experience, and location. The analysis included data cleaning, visualization, and statistical modeling to
identify patterns and insights in the data. The results of the analysis can help both job seekers and employers better understand the factors that
influence data science salaries